203 lines
8.8 KiB
Python
203 lines
8.8 KiB
Python
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import numpy as np
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import pytest
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from numpy.testing import assert_allclose
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from numpy.testing import assert_array_equal
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from sklearn.ensemble._hist_gradient_boosting.histogram import (
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_build_histogram_naive,
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_build_histogram,
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_build_histogram_no_hessian,
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_build_histogram_root_no_hessian,
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_build_histogram_root,
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_subtract_histograms
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)
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from sklearn.ensemble._hist_gradient_boosting.common import HISTOGRAM_DTYPE
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from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE
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from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE
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@pytest.mark.parametrize(
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'build_func', [_build_histogram_naive, _build_histogram])
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def test_build_histogram(build_func):
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binned_feature = np.array([0, 2, 0, 1, 2, 0, 2, 1], dtype=X_BINNED_DTYPE)
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# Small sample_indices (below unrolling threshold)
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ordered_gradients = np.array([0, 1, 3], dtype=G_H_DTYPE)
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ordered_hessians = np.array([1, 1, 2], dtype=G_H_DTYPE)
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sample_indices = np.array([0, 2, 3], dtype=np.uint32)
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hist = np.zeros((1, 3), dtype=HISTOGRAM_DTYPE)
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build_func(0, sample_indices, binned_feature, ordered_gradients,
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ordered_hessians, hist)
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hist = hist[0]
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assert_array_equal(hist['count'], [2, 1, 0])
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assert_allclose(hist['sum_gradients'], [1, 3, 0])
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assert_allclose(hist['sum_hessians'], [2, 2, 0])
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# Larger sample_indices (above unrolling threshold)
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sample_indices = np.array([0, 2, 3, 6, 7], dtype=np.uint32)
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ordered_gradients = np.array([0, 1, 3, 0, 1], dtype=G_H_DTYPE)
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ordered_hessians = np.array([1, 1, 2, 1, 0], dtype=G_H_DTYPE)
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hist = np.zeros((1, 3), dtype=HISTOGRAM_DTYPE)
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build_func(0, sample_indices, binned_feature, ordered_gradients,
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ordered_hessians, hist)
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hist = hist[0]
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assert_array_equal(hist['count'], [2, 2, 1])
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assert_allclose(hist['sum_gradients'], [1, 4, 0])
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assert_allclose(hist['sum_hessians'], [2, 2, 1])
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def test_histogram_sample_order_independence():
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# Make sure the order of the samples has no impact on the histogram
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# computations
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rng = np.random.RandomState(42)
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n_sub_samples = 100
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n_samples = 1000
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n_bins = 256
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binned_feature = rng.randint(0, n_bins - 1, size=n_samples,
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dtype=X_BINNED_DTYPE)
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sample_indices = rng.choice(np.arange(n_samples, dtype=np.uint32),
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n_sub_samples, replace=False)
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ordered_gradients = rng.randn(n_sub_samples).astype(G_H_DTYPE)
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hist_gc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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_build_histogram_no_hessian(0, sample_indices, binned_feature,
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ordered_gradients, hist_gc)
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ordered_hessians = rng.exponential(size=n_sub_samples).astype(G_H_DTYPE)
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hist_ghc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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_build_histogram(0, sample_indices, binned_feature,
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ordered_gradients, ordered_hessians, hist_ghc)
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permutation = rng.permutation(n_sub_samples)
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hist_gc_perm = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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_build_histogram_no_hessian(0, sample_indices[permutation],
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binned_feature, ordered_gradients[permutation],
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hist_gc_perm)
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hist_ghc_perm = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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_build_histogram(0, sample_indices[permutation], binned_feature,
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ordered_gradients[permutation],
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ordered_hessians[permutation], hist_ghc_perm)
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hist_gc = hist_gc[0]
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hist_ghc = hist_ghc[0]
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hist_gc_perm = hist_gc_perm[0]
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hist_ghc_perm = hist_ghc_perm[0]
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assert_allclose(hist_gc['sum_gradients'], hist_gc_perm['sum_gradients'])
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assert_array_equal(hist_gc['count'], hist_gc_perm['count'])
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assert_allclose(hist_ghc['sum_gradients'], hist_ghc_perm['sum_gradients'])
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assert_allclose(hist_ghc['sum_hessians'], hist_ghc_perm['sum_hessians'])
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assert_array_equal(hist_ghc['count'], hist_ghc_perm['count'])
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@pytest.mark.parametrize("constant_hessian", [True, False])
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def test_unrolled_equivalent_to_naive(constant_hessian):
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# Make sure the different unrolled histogram computations give the same
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# results as the naive one.
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rng = np.random.RandomState(42)
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n_samples = 10
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n_bins = 5
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sample_indices = np.arange(n_samples).astype(np.uint32)
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binned_feature = rng.randint(0, n_bins - 1, size=n_samples, dtype=np.uint8)
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ordered_gradients = rng.randn(n_samples).astype(G_H_DTYPE)
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if constant_hessian:
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ordered_hessians = np.ones(n_samples, dtype=G_H_DTYPE)
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else:
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ordered_hessians = rng.lognormal(size=n_samples).astype(G_H_DTYPE)
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hist_gc_root = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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hist_ghc_root = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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hist_gc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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hist_ghc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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hist_naive = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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_build_histogram_root_no_hessian(0, binned_feature,
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ordered_gradients, hist_gc_root)
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_build_histogram_root(0, binned_feature, ordered_gradients,
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ordered_hessians, hist_ghc_root)
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_build_histogram_no_hessian(0, sample_indices, binned_feature,
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ordered_gradients, hist_gc)
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_build_histogram(0, sample_indices, binned_feature,
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ordered_gradients, ordered_hessians, hist_ghc)
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_build_histogram_naive(0, sample_indices, binned_feature,
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ordered_gradients, ordered_hessians, hist_naive)
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hist_naive = hist_naive[0]
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hist_gc_root = hist_gc_root[0]
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hist_ghc_root = hist_ghc_root[0]
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hist_gc = hist_gc[0]
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hist_ghc = hist_ghc[0]
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for hist in (hist_gc_root, hist_ghc_root, hist_gc, hist_ghc):
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assert_array_equal(hist['count'], hist_naive['count'])
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assert_allclose(hist['sum_gradients'], hist_naive['sum_gradients'])
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for hist in (hist_ghc_root, hist_ghc):
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assert_allclose(hist['sum_hessians'], hist_naive['sum_hessians'])
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for hist in (hist_gc_root, hist_gc):
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assert_array_equal(hist['sum_hessians'], np.zeros(n_bins))
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@pytest.mark.parametrize("constant_hessian", [True, False])
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def test_hist_subtraction(constant_hessian):
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# Make sure the histogram subtraction trick gives the same result as the
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# classical method.
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rng = np.random.RandomState(42)
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n_samples = 10
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n_bins = 5
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sample_indices = np.arange(n_samples).astype(np.uint32)
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binned_feature = rng.randint(0, n_bins - 1, size=n_samples, dtype=np.uint8)
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ordered_gradients = rng.randn(n_samples).astype(G_H_DTYPE)
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if constant_hessian:
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ordered_hessians = np.ones(n_samples, dtype=G_H_DTYPE)
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else:
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ordered_hessians = rng.lognormal(size=n_samples).astype(G_H_DTYPE)
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hist_parent = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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if constant_hessian:
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_build_histogram_no_hessian(0, sample_indices, binned_feature,
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ordered_gradients, hist_parent)
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else:
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_build_histogram(0, sample_indices, binned_feature,
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ordered_gradients, ordered_hessians, hist_parent)
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mask = rng.randint(0, 2, n_samples).astype(bool)
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sample_indices_left = sample_indices[mask]
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ordered_gradients_left = ordered_gradients[mask]
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ordered_hessians_left = ordered_hessians[mask]
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hist_left = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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if constant_hessian:
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_build_histogram_no_hessian(0, sample_indices_left,
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binned_feature, ordered_gradients_left,
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hist_left)
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else:
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_build_histogram(0, sample_indices_left, binned_feature,
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ordered_gradients_left, ordered_hessians_left,
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hist_left)
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sample_indices_right = sample_indices[~mask]
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ordered_gradients_right = ordered_gradients[~mask]
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ordered_hessians_right = ordered_hessians[~mask]
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hist_right = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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if constant_hessian:
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_build_histogram_no_hessian(0, sample_indices_right,
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binned_feature, ordered_gradients_right,
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hist_right)
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else:
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_build_histogram(0, sample_indices_right, binned_feature,
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ordered_gradients_right, ordered_hessians_right,
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hist_right)
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hist_left_sub = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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hist_right_sub = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE)
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_subtract_histograms(0, n_bins, hist_parent, hist_right, hist_left_sub)
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_subtract_histograms(0, n_bins, hist_parent, hist_left, hist_right_sub)
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for key in ('count', 'sum_hessians', 'sum_gradients'):
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assert_allclose(hist_left[key], hist_left_sub[key], rtol=1e-6)
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assert_allclose(hist_right[key], hist_right_sub[key], rtol=1e-6)
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